A branch-and-bound algorithm with growing datasets for large-scale parameter estimation
提出一种利用空间分支定界算法求解非凸参数估计问题的方法，通过从缩减数据集开始逐步扩增到完整数据集，在保证收敛到全局最优解的同时节省计算量，并基于开源求解器MAiNGO实现。
The solution of nonconvex parameter estimation problems with deterministic global optimization methods is desirable but challenging, especially if large measurement datasets are considered. We propose to exploit the structure of this class of optimization problems to enable their solution with the spatial branch-and-bound algorithm. In detail, we start with a reduced dataset in the root node and progressively augment it, converging to the full dataset. We show for nonlinear programs (NLPs) that our algorithm converges to the global solution of the original problem considering the full dataset. The implementation of the algorithm extends our open-source solver MAiNGO. A numerical case study with a mixed-integer nonlinear program (MINLP) from chemical engineering and a dynamic optimization problem from biochemistry both using noise-free measurement data emphasizes the potential for savings of computational effort with our proposed approach.